Why black-box AI screening fails candidate trust
Why arbitrary AI percentage scores harm recruiting fairness, and how transparent multi-factor rubrics make hiring decisions defensible.
Practical articles on document parsing, objective candidate scoring, system security, and transparent evaluation architectures.
A breakdown of deterministic text extraction, font layout normalization, and skill canonicalization without hallucinated candidate details.
Why arbitrary AI percentage scores harm recruiting fairness, and how transparent multi-factor rubrics make hiring decisions defensible.
How fixed topic sequences, calibrated rubric scoring, and transcript verification keep panel evaluations consistent across diverse applicant pools.
Why public S3/R2 storage buckets expose sensitive applicant data, and how we restrict resume access using cryptographically signed tokens.
Understanding technology relationships (e.g. Docker and Kubernetes, PyTorch and JAX) to accurately evaluate candidates with adjacent experience.
Common communication pitfalls in technical rounds, and how active verbal simulation helps engineers clearly explain complex systems.
If your engineering team has insights on objective evaluation, ATS optimization, or interviewer training, we'd love to publish your experience.